Artificial intelligence supporting content delivery

Abstract

According to one embodiment, a method, computer system, and computer program product for gathering relevant digital content. The embodiment may include receiving, from an online meeting scheduler, information within an online meeting invite. The embodiment may include analyzing the information using natural language processing (NLP) and machine learning (ML) techniques. Based on results of the analysis of the received information, the embodiment may include locating additional digital content items of the user which are relevant to the information of the online meeting invite. The embodiment may include outputting, to a user, a top-k listing of the additional digital content items for attachment to the online meeting invite before being sent.

What is claimed is: 
     
       1. A computer-implemented method, the method comprising:
 receiving, from an online meeting scheduler, information within an online meeting invite; 
 analyzing the information using natural language processing (NLP) and machine learning (ML) techniques, wherein the information comprises a meeting time; 
 based on results of the analysis of the received information and in response to having reached a threshold time before the meeting time, locating additional digital content items of the user which are relevant to the information of the online meeting invite; 
 outputting, to a user, a top-k listing of the additional digital content items for attachment to the online meeting invite before being sent; 
 attaching, to the online meeting invite, a digital content item selected by the user from the top-k listing, wherein the attaching augments the information of the online meeting invite; and 
 iteratively performing the analyzing, the locating, and the outputting until the top-k listing is the same on consecutive iterations and the user does not select any additional content items of the top-k listing for attachment. 
 
     
     
       2. The method of  claim 1 , further comprising:
 outputting, to the user, a top-k listing of the additional digital content items for review prior to the meeting time of the online meeting invite. 
 
     
     
       3. The method of  claim 1 , wherein analyzing the information using NLP and ML techniques further comprises:
 parsing the information as unstructured text; 
 applying domain specific weights to the parsed information; 
 performing supervised learning using the weighted parsed information; and 
 creating an eigenvector and a corresponding eigenvalue for the online meeting invite based on output from the performed supervised learning. 
 
     
     
       4. The method of  claim 3 , wherein locating the additional digital content items comprises searching a local or a remote digital content repository of the user, and wherein relevance of additional digital content items to the information of the online meeting invite is based on a level of similarity between the eigenvector and the corresponding eigenvalue of the online meeting invite and respective eigenvectors and corresponding eigenvalues of the additional digital content items. 
     
     
       5. The method of  claim 4 , wherein the respective eigenvectors and corresponding eigenvalues of the additional digital content items are calculated upon receipt of each of the additional digital content items by the user. 
     
     
       6. The method of  claim 1 , wherein an additional digital content item is selected from the group consisting of an online meeting invite of the user, an email of the user, and a data file of the user. 
     
     
       7. The method of  claim 1 , further comprising:
 sending the online meeting invite to one or more meeting invitees; and 
 receiving feedback from the user concerning the top-k listing of the additional digital content items. 
 
     
     
       8. A computer system, the computer system comprising:
 one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising:
 receiving, from an online meeting scheduler, information within an online meeting invite; 
 analyzing the information using natural language processing (NLP) and machine learning (ML) techniques, wherein the information comprises a meeting time; 
 based on results of the analysis of the received information and in response to having reached a threshold time before the meeting time, locating additional digital content items of the user which are relevant to the information of the online meeting invite; 
 outputting, to a user, a top-k listing of the additional digital content items for attachment to the online meeting invite before being sent; 
 attaching, to the online meeting invite, a digital content item selected by the user from the top-k listing, wherein the attaching augments the information of the online meeting invite; and 
 iteratively performing the analyzing, the locating, and the outputting until the top-k listing is the same on consecutive iterations and the user does not select any additional content items of the top-k listing for attachment. 
 
 
     
     
       9. The computer system of  claim 8 , further comprising:
 outputting, to the user, a top-k listing of the additional digital content items for review prior to the meeting time of the online meeting invite. 
 
     
     
       10. The computer system of  claim 8 , wherein analyzing the information using NLP and ML techniques further comprises:
 parsing the information as unstructured text; 
 applying domain specific weights to the parsed information; 
 performing supervised learning using the weighted parsed information; and 
 creating an eigenvector and a corresponding eigenvalue for the online meeting invite based on output from the performed supervised learning. 
 
     
     
       11. The computer system of  claim 10 , wherein locating the additional digital content items comprises searching a local or a remote digital content repository of the user, and wherein relevance of additional digital content items to the information of the online meeting invite is based on a level of similarity between the eigenvector and corresponding eigenvalue of the online meeting invite and respective eigenvectors and corresponding eigenvalues of the additional digital content items. 
     
     
       12. The computer system of  claim 11 , wherein the respective eigenvectors and corresponding eigenvalues of the additional digital content items are calculated upon receipt of each of the additional digital content items by the user. 
     
     
       13. The computer system of  claim 8 , wherein an additional digital content item is selected from the group consisting of an online meeting invite of the user, an email of the user, and a data file of the user. 
     
     
       14. The computer system of  claim 8 , further comprising:
 sending the online meeting invite to one or more meeting invitees; and 
 receiving feedback from the user concerning the top-k listing of the additional digital content items. 
 
     
     
       15. A computer program product, the computer program product comprising:
 one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more tangible storage medium, the program instructions executable by a processor capable of performing a method, the method comprising;
 receiving, from an online meeting scheduler, information within an online meeting invite; 
 analyzing the information using natural language processing (NLP) and machine learning (ML) techniques, wherein the information comprises a meeting time; 
 based on results of the analysis of the received information and in response to having reached a threshold time before the meeting time, locating additional digital content items of the user which are relevant to the information of the online meeting invite; 
 outputting, to a user, a top-k listing of the additional digital content items for attachment to the online meeting invite before being sent; 
 attaching, to the online meeting invite, a digital content item selected by the user from the top-k listing, wherein the attaching augments the information of the online meeting invite; and 
 iteratively performing the analyzing, the locating, and the outputting until the top-k listing is the same on consecutive iterations and the user does not select any additional content items of the top-k listing for attachment. 
 
 
     
     
       16. The computer program product of  claim 15 , further comprising:
 outputting, to the user, a top-k listing of the additional digital content items for review prior to the meeting time of the online meeting invite. 
 
     
     
       17. The computer program product of  claim 15 , wherein analyzing the information using NLP and ML techniques further comprises:
 parsing the information as unstructured text; 
 applying domain specific weights to the parsed information; 
 performing supervised learning using the weighted parsed information; and 
 creating an eigenvector and a corresponding eigenvalue for the online meeting invite based on output from the performed supervised learning. 
 
     
     
       18. The computer program product of  claim 17 , wherein locating the additional digital content items comprises searching a local or a remote digital content repository of the user, and wherein relevance of additional digital content items to the information of the online meeting invite is based on a level of similarity between the eigenvector and corresponding eigenvalue of the online meeting invite and respective eigenvectors and corresponding eigenvalues of the additional digital content items. 
     
     
       19. The computer program product of  claim 18 , wherein the respective eigenvectors and corresponding eigenvalues of the additional digital content items are calculated upon receipt of each of the additional digital content items by the user. 
     
     
       20. The computer program product of  claim 15 , further comprising:
 sending the online meeting invite to one or more meeting invitees; and 
 receiving feedback from the user concerning the top-k listing of the additional digital content items.